{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fashion MNIST Pixel by Pixel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Re-purposes the [MNIST Digit Pixel by Pixel]() notebook for the purposes of plotting an example Fashion-MNIST image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/the-deep-learners/deep-learning-illustrated/blob/master/notebooks/fashion_mnist_pixel_by_pixel.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from keras.datasets import fashion_mnist\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_valid, y_valid) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sample an image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample = np.random.randint(0, X_train.shape[0])\n",
    "sample = 39235"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot digit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc85e1a8128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "mnist_img = X_train[sample]\n",
    "plt.imshow(mnist_img,cmap=\"Greys\")\n",
    "ax = plt.gca()\n",
    "\n",
    "# First turn off the  major labels, but not the major ticks\n",
    "plt.tick_params(\n",
    "    axis='both',        # changes apply to the both x and y axes\n",
    "    which='major',      # Change the major ticks only\n",
    "    bottom=True,        # ticks along the bottom edge are on\n",
    "    left=True,          # ticks along the top edge are on\n",
    "    labelbottom=False,  # labels along the bottom edge are off\n",
    "    labelleft=False)    # labels along the left edge are off\n",
    "\n",
    "# Next turn off the minor ticks, but not the minor labels\n",
    "plt.tick_params(\n",
    "    axis='both',        # changes apply to both x and y axes\n",
    "    which='minor',      # Change the minor ticks only\n",
    "    bottom=False,       # ticks along the bottom edge are off\n",
    "    left=False,         # ticks along the left edge are off\n",
    "    labelbottom=True,   # labels along the bottom edge are on\n",
    "    labelleft=True)     # labels along the left edge are on\n",
    "\n",
    "# Set the major ticks, starting at 1 (the -0.5 tick gets hidden off the canvas)\n",
    "ax.set_xticks(np.arange(-.5, 28, 1))\n",
    "ax.set_yticks(np.arange(-.5, 28, 1))\n",
    "\n",
    "# Set the minor ticks and labels\n",
    "ax.set_xticks(np.arange(0, 28, 1), minor=True);\n",
    "ax.set_xticklabels([str(i) for i in np.arange(0, 28, 1)], minor=True);\n",
    "ax.set_yticks(np.arange(0, 28, 1), minor=True);\n",
    "ax.set_yticklabels([str(i) for i in np.arange(0, 28, 1)], minor=True);\n",
    "\n",
    "ax.grid(color='black', linestyle='-', linewidth=1.5)\n",
    "_ = plt.colorbar(fraction=0.046, pad=0.04, ticks=[0,32,64,96,128,160,192,224,255])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confirm image label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[sample]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
